Vascular cognitive impairment is an umbrella term for cognitive dysfunction associated with and presumed to be caused by vascular brain damage. Autopsy studies have identified microinfarcts as an important neuropathological correlate of vascular cognitive impairment that escapes detection by conventional magnetic resonance imaging (MRI). As a frame of reference for future highresolution MRI studies, we systematically reviewed the literature on neuropathological studies on cerebral microinfarcts in the context of vascular disease, vascular risk factors, cognitive decline and dementia. We identified 32 original patient studies involving 10,515 people. The overall picture is that microinfarcts are common, particularly in patients with vascular dementia (weighted average 62%), Alzheimer's disease (43%), and demented patients with both Alzheimer-type and cerebrovascular pathology (33%) compared with nondemented older individuals (24%). In many patients, multiple microinfarcts were detected. Microinfarcts are described as minute foci with neuronal loss, gliosis, pallor, or more cystic lesions. They are found in all brain regions, possibly more so in the cerebral cortex, particularly in watershed areas. Reported sizes vary from 50 lm to a few mm, which is within the detection limit of current high-resolution MRI. Detection of these lesions in vivo would have a high potential for future pathophysiological studies in vascular cognitive impairment.

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://wmh.isi.uu.nl/). Sixty T1+FLAIR images from three MR scanners were released with manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. Segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: (1) Dice similarity coefficient, (2) modified Hausdorff distance (95th percentile), (3) absolute log-transformed volume difference, (4) sensitivity for detecting individual lesions, and (5) F1-score for individual lesions. Additionally, methods were ranked on their inter-scanner robustness.Twenty participants submitted their method for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all methods generalize to unseen scanners.The challenge remains open for future submissions and provides a public platform for method evaluation.

OBJECTIVETo examine whether type 2 diabetes is associated with microstructural abnormalities in specific cerebral white matter tracts and to relate these microstructural abnormalities to cognitive functioning.RESEARCH DESIGN AND METHODSThirty-five nondemented older individuals with type 2 diabetes (mean age 71 ± 5 years) and 35 age-, sex-, and education-matched control subjects underwent a 3 Tesla diffusion-weighted MRI scan and a detailed cognitive assessment. Tractography was performed to reconstruct several white matter tracts. Diffusion tensor imaging measures, including fractional anisotropy (FA) and mean diffusivity (MD), were compared between groups and related to cognitive performance.RESULTSMD was significantly increased in all tracts in both hemispheres in patients compared with control subjects (P < 0.05), reflecting microstructural white matter abnormalities in the diabetes group. Increased MD was associated with slowing of information-processing speed and worse memory performance in the diabetes but not in the control group after adjustment for age, sex, and estimated IQ (group × MD interaction, all P < 0.05). These associations were independent of total white matter hyperintensity load and presence of cerebral infarcts.CONCLUSIONSIndividuals with type 2 diabetes showed microstructural abnormalities in various white matter pathways. These abnormalities were related to worse cognitive functioning.

Aims/hypothesisType 2 diabetes mellitus is associated with moderate decrements in cognitive functioning, mainly in verbal memory, information-processing speed and executive functions. How this cognitive profile evolves over time is uncertain. The present study aims to provide detailed information on the evolution of cognitive decrements in type 2 diabetes over time.MethodsSixty-eight patients with type 2 diabetes and 38 controls matched for age, sex and estimated IQ performed an elaborate neuropsychological examination in 2002–2004 and again in 2006–2008, including 11 tasks covering five cognitive domains. Vascular and metabolic determinants were recorded. Data were analysed with repeated measures analysis of variance, including main effects for group, time and the group × time interaction.ResultsPatients with type 2 diabetes showed moderate decrements in information-processing speed (mean difference in z scores [95% CI] −0.37 [−0.69, −0.05]) and attention and executive functions (−0.25 [−0.49, −0.01]) compared with controls at both the baseline and the 4 year follow-up examination. After 4 years both groups showed a decline in abstract reasoning (−0.16 [−0.30, −0.02]) and attention and executive functioning (−0.29 [−0.40, −0.17]), but there was no evidence for accelerated cognitive decline in the patients with type 2 diabetes as compared with controls (all p > 0.05).Conclusions/interpretationIn non-demented patients with type 2 diabetes, cognitive decrements are moderate in size and cognitive decline over 4 years is largely within the range of what can be viewed in normal ageing. Apparently, diabetes-related cognitive changes develop slowly over a prolonged period of time.

OBJECTIVEType 2 diabetes is associated with a moderate degree of cerebral atrophy and a higher white matter hyperintensity (WMH) volume. How these brain-imaging abnormalities evolve over time is unknown. The present study aims to quantify cerebral atrophy and WMH progression over 4 years in type 2 diabetes.RESEARCH DESIGN AND METHODSA total of 55 patients with type 2 diabetes and 28 age-, sex-, and IQ-matched control participants had two 1.5T magnetic resonance imaging scans with a 4-year interval. Volumetric measurements of total brain, peripheral cerebrospinal fluid (CSF), lateral ventricles, and WMH were performed with k-nearest neighbor–based probabilistic segmentation. All volumes were expressed as percentage of intracranial volume. Linear regression analyses, adjusted for age and sex, were performed to compare brain volumes between the groups and to identify determinants of volumetric change within the type 2 diabetic group.RESULTSAt baseline, patients with type 2 diabetes had a significantly smaller total brain volume and larger peripheral CSF volume than control participants. In both groups, all volumes showed a significant change over time. Patients with type 2 diabetes had a greater increase in lateral ventricular volume than control participants (mean adjusted between-group difference in change over time [95% CI]: 0.11% in 4 years [0.00 to 0.22], P = 0.047).CONCLUSIONSThe greater increase in lateral ventricular volume over time in patients with type 2 diabetes compared with control participants shows that type 2 diabetes is associated with a slow increase of cerebral atrophy over the course of years.

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

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